OpenRaiser/Intern-Atlas huggingface dataset:
Destruction of Image Steganography using Generative Adversarial Networks
Venue: arXiv cs.MM 2019
Year: 2019

Digital image steganalysis, or the detection of image steganography, has been studied in depth for years and is driven by Advanced Persistent Threat (APT) groups', such as APT37 Reaper, utilization of steganographic techniques to transmit additional malware to perform further post-exploitation activity on a compromised host. However, many steganalysis algorithms are constrained to work with only a subset of all possible images in the wild or are known to produce a high false positive rate. This results in blocking any suspected image being an unreasonable policy. A more feasible policy is to filter suspicious images prior to reception by the host machine. However, how does one optimally filter specifically to obfuscate or remove image steganography while avoiding degradation of visual image quality in the case that detection of the image was a false positive? We propose the Deep Digital Steganography Purifier (DDSP), a Generative Adversarial Network (GAN) which is optimized to destroy steganographic content without compromising the perceptual quality of the original image. As verified by experimental results, our model is capable of providing a high rate of destruction of steganographic image content while maintaining a high visual quality in comparison to other state-of-the-art filtering methods. Additionally, we test the transfer learning capability of generalizing to to obfuscate real malware payloads embedded into different image file formats and types using an unseen steganographic algorithm and prove that our model can in fact be deployed to provide adequate results.
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Venue: arXiv cs.CV 2019
Year: 2019

Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily. Unfortunately, popular NR prediction models perform poorly on real-world distorted pictures. To advance progress on this problem, we introduce the largest (by far) subjective picture quality database, containing about 40000 real-world distorted pictures and 120000 patches, on which we collected about 4M human judgments of picture quality. Using these picture and patch quality labels, we built deep region-based architectures that learn to produce state-of-the-art global picture quality predictions as well as useful local picture quality maps. Our innovations include picture quality prediction architectures that produce global-to-local inferences as well as local-to-global inferences (via feedback).
Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog
Venue: arXiv cs.MM 2019
Year: 2019

With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of many homes. Currently, such IVAs are mostly audio-based, but going forward, we are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances. This will enable agents to have conversations with users about the objects, activities and events surrounding them. In this work, we present three main architectural explorations for the Audio Visual Scene-Aware Dialog (AVSD): 1) investigating `topics' of the dialog as an important contextual feature for the conversation, 2) exploring several multimodal attention mechanisms during response generation, 3) incorporating an end-to-end audio classification ConvNet, AclNet, into our architecture. We discuss detailed analysis of the experimental results and show that our model variations outperform the baseline system presented for the AVSD task.
QuB: A Resource Aware Functional Programming Language
Venue: arXiv cs.PL 2019
Year: 2019

Managing resources---file handles, database connections, etc.---is a hard problem. Debugging resource leaks and runtime errors due to resource mismanagement are difficult in evolving production code. Programming languages with static type systems are great tools to ensure erroneous code is detected at compile time. However, modern static type systems do little in the aspect of resource management as resources are treated as normal values. We propose a type system, Qub, based on the logic of bunched implications (BI) which models resources as first class citizens. We distinguish two kinds of program objects---restricted and unrestricted---and two kinds of functions---sharing and separating. Our approach guarantees resource correctness without compromising existing functional abstractions.
String factorisations with maximum or minimum dimension
Venue: arXiv cs.DS 2019
Year: 2019

In this paper we consider two problems concerning string factorisation. Specifically given a string $w$ and an integer $k$ find a factorisation of $w$ where each factor has length bounded by $k$ and has the minimum (the FmD problem) or the maximum (the FMD problem) number of different factors. The FmD has been proved to be NP-hard even if $k=2$ in [9] and for this case we provide a $3/2$-approximation algorithm. The FMD problem, up to our knowledge has not been considered in the literature. We show that this problem is NP-hard for any $k\geq 3$. In view of this we propose a $2$-approximation algorithm (for any $k$) an exact exponential algorithm. We conclude with some open problems.
Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions
Venue: arXiv cs.CV 2019
Year: 2019

Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which not only can generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.
Measuring Dataset Granularity
Venue: arXiv cs.CV 2019
Year: 2019

Despite the increasing visibility of fine-grained recognition in our field, "fine-grained'' has thus far lacked a precise definition. In this work, building upon clustering theory, we pursue a framework for measuring dataset granularity. We argue that dataset granularity should depend not only on the data samples and their labels, but also on the distance function we choose. We propose an axiomatic framework to capture desired properties for a dataset granularity measure and provide examples of measures that satisfy these properties. We assess each measure via experiments on datasets with hierarchical labels of varying granularity. When measuring granularity in commonly used datasets with our measure, we find that certain datasets that are widely considered fine-grained in fact contain subsets of considerable size that are substantially more coarse-grained than datasets generally regarded as coarse-grained. We also investigate the interplay between dataset granularity with a variety of factors and find that fine-grained datasets are more difficult to learn from, more difficult to transfer to, more difficult to perform few-shot learning with, and more vulnerable to adversarial attacks.
Replication in Data Grids: Metrics and Strategies
Venue: arXiv cs.DB 2019
Year: 2019

We focus in this report on two main axes. The first is dedicated to the study of the effect of replicas distribution on data grid performances. In this respect, our main contributions are as follows: 1) An overview of replication strategies mainly from the viewpoints of the considered parameters in their associated steps as well as the used metrics in the literature for their evaluation. 2) A study of the impact of placement strategies on data grid performance which motivated the analysis of the effect of the replicas distribution quality on the performance results of replication strategies. 3) The proposal of new evaluation metrics dedicated to the evaluation of the distribution quality. 4) The setting of an objective evaluation of replication strategies which is based on a beforehand assessment of the distribution quality. The second axis is mainly dedicated to exploiting results of data mining techniques to enhance performances of replication strategies. With respect to this axis, we mainly concentrate on the following contributions listed below: 1) The study of the strengths and the drawbacks of the main replication strategies based on data mining techniques and how these latter are applied in this context. 2) The proposal of a new guideline to data mining application in the context of data grid replication strategies. 3) The proposal of a new algorithm for mining maximal frequent correlated patterns. The input of this algorithm is obtained through a preliminary step focusing on how to adapt the required grid concepts to the data mining algorithm. 4) The design and the implementation of a new replication strategy based on a data mining technique, and more precisely correlated patterns.
Power Control for Massive MIMO Systems with Nonorthogonal Pilots
Venue: arXiv cs.IT 2019
Year: 2019

This letter shows that optimizing the transmit powers along with optimally designed nonorthogonal pilots can significantly reduce pilot contamination and improve the overall throughput of the uplink multi-cell massive multiple-input multiple-output (MIMO) system as compared to the conventional schemes that use orthogonal pilots. Given the optimized nonorthogonal pilots, power control as a function of the large-scale path-loss can be thought of as a stochastic optimization problem due to the presence of fast fading. This paper advocates a deterministic approach to solve this problem, then further proposes a stochastic optimization method that utilizes successive convex approximation as a benchmark to quantify the performance of the proposed approach. Simulation results reveal significant advantage of using optimized nonorthogonal pilots together with power control to combat pilot contamination.
Eliminating cross-camera bias for vehicle re-identification
Venue: arXiv cs.CV 2019
Year: 2019

Vehicle re-identification (reID) often requires recognize a target vehicle in large datasets captured from multi-cameras. It plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic in recent years. However, the appearance of vehicle images is easily affected by the environment that various illuminations, different backgrounds and viewpoints, which leads to the large bias between different cameras. To address this problem, this paper proposes a cross-camera adaptation framework (CCA), which smooths the bias by exploiting the common space between cameras for all samples. CCA first transfers images from multi-cameras into one camera to reduce the impact of the illumination and resolution, which generates the samples with the similar distribution. Then, to eliminate the influence of background and focus on the valuable parts, we propose an attention alignment network (AANet) to learn powerful features for vehicle reID. Specially, in AANet, the spatial transfer network with attention module is introduced to locate a series of the most discriminative regions with high-attention weights and suppress the background. Moreover, comprehensive experimental results have demonstrated that our proposed CCA can achieve excellent performances on benchmark datasets VehicleID and VeRi-776.
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Venue: arXiv cs.LG 2019
Year: 2019

Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. Most existing work has either limited long-term prediction performance or lacks a comprehensive ability to capture spatio-temporal dependencies in data. Accurate and early disease forecasting models would markedly improve both epidemic prevention and managing the onset of an epidemic. In this paper, we design a cross-location attention based graph neural network (Cola-GNN) for learning time series embeddings and location aware attentions. We propose a graph message passing framework to combine learned feature embeddings and an attention matrix to model disease propagation over time. We compare the proposed method with state-of-the-art statistical approaches and deep learning models on real-world epidemic-related datasets from United States and Japan. The proposed method shows strong predictive performance and leads to interpretable results for long-term epidemic predictions.
Influence in Weighted Committees
Venue: arXiv cs.GT 2019
Year: 2019

A committee's decisions on more than two alternatives much depend on the adopted voting method, and so does the distribution of power among the committee members. We investigate how different aggregation methods such as plurality runoff, Borda count, or Copeland rule map asymmetric numbers of seats, shares, voting weights, etc. to influence on outcomes when preferences vary. A generalization of the Penrose-Banzhaf power index is proposed and applied to the IMF Executive Board's election of a Managing Director, extending a priori voting power analysis from binary simple voting games to choice in weighted committees.
Passive Beamforming and Information Transfer Design for Reconfigurable Intelligent Surfaces Aided Multiuser MIMO Systems
Venue: arXiv cs.IT 2019
Year: 2019

This paper investigates the passive beamforming and information transfer (PBIT) technique for the multiuser multiple-input multiple-output (MIMO) systems with the aid of a reconfigurable intelligent surface (RIS), where the RIS enhances the primary communication via passive beamforming and at the same time delivers additional information by the spatial modulation (which adjusts the on-off states of the reflecting elements). For the passive beamforming design, we propose to maximize the sum channel capacity of the RIS-aided multiuser MIMO channel and formulate the problem as a two-step stochastic program. A sample average approximation (SAA) based iterative algorithm is developed for the efficient passive beamforming design of the considered scheme. To strike a balance between complexity and performance, we then propose a simplified beamforming algorithm by approximating the stochastic program as a deterministic alternating optimization problem. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the RIS information is multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this bilinear estimation problem, we develop a turbo message passing (TMP) algorithm in which the factor graph associated with the problem is divided into two modules: one for the estimation of the user signals and the other for the estimation of the RIS's on-off states. The two modules are executed iteratively to yield a near-optimal low-complexity solution. Furthermore, we extend the design of the multiuser MIMO PBIT scheme from single-RIS to multi-RIS, by leveraging the similarity between the single-RIS and multi-RIS system models. Extensive simulation results are provided to demonstrate the advantages of our passive beamforming and receiver designs.
A sparse resultant based method for efficient minimal solvers
Venue: arXiv cs.CV 2019
Year: 2019

Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e. solving minimal problems in a RANSAC framework. Minimal problems often result in complex systems of polynomial equations. Many state-of-the-art efficient polynomial solvers to these problems are based on Gröbner bases and the action-matrix method that has been automatized and highly optimized in recent years. In this paper we study an alternative algebraic method for solving systems of polynomial equations, i.e., the sparse resultant-based method and propose a novel approach to convert the resultant constraint to an eigenvalue problem. This technique can significantly improve the efficiency and stability of existing resultant-based solvers. We applied our new resultant-based method to a large variety of computer vision problems and show that for most of the considered problems, the new method leads to solvers that are the same size as the the best available Gröbner basis solvers and of similar accuracy. For some problems the new sparse-resultant based method leads to even smaller and more stable solvers than the state-of-the-art Gröbner basis solvers. Our new method can be fully automatized and incorporated into existing tools for automatic generation of efficient polynomial solvers and as such it represents a competitive alternative to popular Gröbner basis methods for minimal problems in computer vision.
Socio-network Analysis of RTL Designs for Hardware Trojan Localization
Venue: arXiv cs.CR 2019
Year: 2019

The recent surge in hardware security is significant due to offshoring the proprietary Intellectual property (IP). One distinct dimension of the disruptive threat is malicious logic insertion, also known as Hardware Trojan (HT). HT subverts the normal operations of a device stealthily. The diversity in HTs activation mechanisms and their location in design brings no catch-all detection techniques. In this paper, we propose to leverage principle features of social network analysis to security analysis of Register Transfer Level (RTL) designs against HT. The approach is based on investigating design properties, and it extends the current detection techniques. In particular, we perform both node- and graph-level analysis to determine the direct and indirect interactions between nets in a design. This technique helps not only in finding vulnerable nets that can act as HT triggering signals but also their interactions to influence a particular net to act as HT payload signal. We experiment the technique on 420 combinational HT instances, and on average, we can detect both triggering and payload signals with accuracy up to 97.37%.
Guidelines for 5G End to End Architecture and Security Issues
Venue: arXiv cs.NI 2019
Year: 2019

Hackers target their attacks on the most vulnerable parts of a system. A system is therefore only as strong as its weakest part, similar to the Cannikin Law, which states that the capacity of a barrel of water depends on the height of the shortest rather than the longest piece of wood. To ensure the security of 5G networks, we first need to understand the overall 5G architecture and examine the potential threats instead of merely setting up a firewall. However, 5G networks will have tremendous coverage. The development of 5G techniques require extensive resources, leading to intense competition between countries attempting to develop 5G networks. Many outstanding papers discuss the techniques for developing specific aspects of the 5G architecture, but to the best of our knowledge, few provide an overview of a complete 5G network. This presents us with a difficult situation because we need to consider the overall architecture to ensure the security of 5G networks. To address that problem, in this paper we provide essential guidelines for understanding the architecture of 5G. We introduce 5G scenarios, outline the network architecture, and highlight the potential security issues for the various components of a 5G network. This paper is intended to facilitate a preliminary understanding of the 5G architecture and the possible security concerns of the various components. The end to end (E2E) 5G network architecture is composed of a next-generation radio access network (NG-RAN), multi-access edge computing (MEC), virtual evolved packet core (vEPC), a data network (DN) and a cloud service. Network slicing (NS), network function virtualization (NFV), NFV Management and Orchestration (MANO) and software-defined networking (SDN) are also important techniques for achieving 5G network architectures.
Tifinagh-IRCAM Handwritten character recognition using Deep learning
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we exploit the benefits of the deep learning approach to design an efficient system of Amazigh handwritten recognition. Indeed, this approach has proved a greater efficiency in the various domains, especially recognition tasks. However, to take full advantage of this approach it's necessary to construct an adequate dataset of training and testing that represent faithfully the concerned problem. To this end, we have prepared our dataset of 102 writers each one contains 33 characters of IRCAM-Tifinagh. Inspired by the MNIST database, the set of characters is size-normalized and centered in a fixed-size image. The resulting is a grey level image of size 28x28, where the black color is the non-color of the character. The number of images produced after this preprocessing step is 3,366.
Microchain: a Light Hierarchical Consensus Protocol for IoT System
Venue: arXiv cs.DC 2019
Year: 2019

While the large-scale Internet of Things (IoT) makes many new applications feasible, like Smart Cities, IoT also brings new concerns on data reliability, security, and privacy. The rapid evolution in blockchain technologies, which relied on a decentralized, immutable and distributed ledger system for transaction data auditing, provides a prospective solution to address the issues in IoT. The blockchain and smart contract enabled security mechanism for IoT applications have attracted increasing interests from both academia and industry. However, integrating cryptocurrency-oriented blockchain technologies into IoT systems meets tremendous challenges on scalability, storage capacity, security, and privacy. Particularly, the performance of blockchain networks significantly relies on the performance of consensus mechanisms, e.g., in terms of data confidentiality, transaction throughput, and network scalability. In this chapter, given an in-depth review of state-of-the-art blockchain networks, the key matrix of designing consensus mechanism for IoT networks are identified in terms of throughput, scalability, and security. To demonstrate a case study on designing scalable, lightweight blockchain protocols for IoT systems, a Microchain framework is introduced and a proof-of-concept prototype is implemented in a physical network environment. The experimental results verify the feasibility of integrating the Microchain into IoT systems.